title: Information-maximization clustering based on squared-loss mutual information.
abstract: Information-maximization clustering learns a probabilistic classifier in an unsupervised manner so that mutual information between feature vectors and cluster assignments is maximized. A notable advantage of this approach is that it involves only continuous optimization of model parameters, which is substantially simpler than discrete optimization of cluster assignments. However, existing methods still involve nonconvex optimization problems, and therefore finding a good local optimal solution is not straightforward in practice. In this letter, we propose an alternative information-maximization clustering method based on a squared-loss variant of mutual information. This novel approach gives a clustering solution analytically in a computationally efficient way via kernel eigenvalue decomposition. Furthermore, we provide a practical model selection procedure that allows us to objectively optimize tuning parameters included in the kernel function. Through experiments, we demonstrate the usefulness of the proposed approach.
authors.affiliation: Tokyo Institute of Technology, Merugo-ku, Tokyo 152-8552, Japan sugi@cs.titech.ac.jp.
authors.forename: Masashi
authors.initials: M
authors.lastname: Sugiyama
authors.forename: Gang
authors.initials: G
authors.lastname: Niu
authors.forename: Makoto
authors.initials: M
authors.lastname: Yamada
authors.forename: Manabu
authors.initials: M
authors.lastname: Kimura
authors.forename: Hirotaka
authors.initials: H
authors.lastname: Hachiya
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D005145:Face; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*